The sugarcane node is difficult to recognize due to the diversity and complexity of surface that different types of sugarcane have. To solve the problem, a sugarcane node recognition method suitable for different types of sugarcane was proposed based on machine vision. Firstly, by the iterative linear fitting algorithm, the target region was extracted from the original image and its slope angle to horizontal axis was estimated. According to the angle, the target was rotated to being nearly parallel to the horizontal axis. Secondly, Double-Density Dual Tree Complex Wavelet Transform (DD-DTCWT) was used to decompose the image, and the image was reconstructed by using the wavelet coefficients that were perpendicular or approximately perpendicular to the horizontal axis. Finally, the line detection algorithm was used to detect the image, and the lines near the sugarcane node were obtained. The recognition was realized by further verifying the density, length and mutual distances of the edge lines. Experimental results show that the complete recognition rate reaches 92%, the localization accuracy of about 80% of nodes is less than 16 pixels, and the localization accuracy of 95% nodes is less than 32 pixels. The proposed method realizes node recognition for different types of sugarcane under different background with high position accuracy.

Focusing on the issues of failing to pinpoint the edge information accurately and lacking texture detail of image by using integer order differential or 0-1-order fractional differential mask operators in digital image processing, a new 1-2-order edge detection operator based on Laplacian operator was proposed. Deduced from the definition of Riemann-Liouville (R-L),the 1-2-order fractional differential had the advantage in enhancing high-frequency signal and reinforcing medium frequency signal. The simulation results demonstrate that the proposed operator can take an higher recognition rate on the subjective recognition, and it's better at extracting the edge information, especially for the image with rich texture detail in the smooth region with little change of gray scale. Objectively, the integrated location error rate is 7.41% which is less than that of integer order differential operators (a minimum of 10.36%) and 0-1-order differential operator (a minimum of 9.97%). Quantitative indicators show the new fractional operator can effectively improve the positioning accuracy of the edge, and the proposed operator is particularly suitable for edge detection with high frequency information.

Concerning the slow searching, poor practicability and being difficult to get a perfectly reasonable options of the methods for solving the problem of cloud services partner selection, a new partner selection method was proposed based on grey relational analysis and Particle Swarm Optimization (PSO) algorithm. Firstly, grey relational analysis method was used to select evaluation indexs of cloud providers, then the weight of each index value was calculated. Secondly, the mathematical model of services partner selection problems in cloud environment was built, then it was solved by using PSO algorithm to find the best partners. Performance test results of specific application examples show that the proposed method is feasible and rational, and can select the best partners.

Focusing on the problem of robust video object tracking, a robust generative algorithm based on sparse representation was proposed. Firstly, object and background templates were constructed by extracting the image features, and sufficient candidates were acquired by using random sampling method at each frame. Secondly, the sparse coefficient vector was got to structure the similarity map by an innovative optimization formulation named multitask reverse sparse representation formulation, which searched multiple subsets from the whole candidate set to simultaneously reconstruct multiple templates with minimum error. Here a customized Augmented Lagrange Multiplier (ALM) method was derived for solving the L ₁-min problem within several iterations. Finally, the additive pooling was proposed to extract discriminative information in the similarity map for effectively selecting the best candidate which the most similar to the object template and was most different to the background template to be the tracking result, and the tracking was implemented within the Bayesian filtering framework. Moreover, a simple but effective update mechanism was made to update object and background templates so as to handle the object appearance variation caused by illumination change, occlusion, background clutter and motion blur. Compared with the other tracking algorithms, both qualitative and quantitative evaluations on a variety of challenging sequences demonstrate that the tracking accuracy and stability of the proposed algorithm has improved and the proposed algorithm can effectively solve target tracking problem in these scenes of illumination and scale changing, occlusion, complex background, and so on.

In order to improve the detection accuracy of network intrusion, a network intrusion detection model named PSO-IG was proposed based on Particle Swarm Optimization (PSO) algorithm and Information Gain (IG). Firstly, PSO algorithm was used to eliminate redundant features of original network data, and then the weight values of selection features were obtained using IG, and Support Vector Machine (SVM) was used to establish intrusion detection model. Finally, the KDD CUP 99 dataset was used to test the performance of PSO-IG. The results show that the proposed model can eliminate redundant features and reduce the input dimension to improve the detection speed of network intrusion, and it can improve the network intrusion detection accuracy by reasonable selecting weight values.

Polymorphic worms signature extraction is a critical part of signature-based intrusion detection. Extracting precise signatures quickly plays an important role in preventing the spread of the worms. Since the classical Hierarchical Multi-Sequence Alignment (HMSA) algorithm has bad time performance in extracting signatures when multiple sequences alignment was used and the extracted signatures were not precise enough, a new automatic signature extraction method called antMSA was proposed based on the improved ant optimal algorithm. The search strategy of the ant group was improved, and then it was introduced to the Contiguous Matches Encouraging Needleman-Wunsch (CMENW) algorithm to get a better solution quickly in global range by using the rapid convergence ability of ant colony algorithm. The signature fragments were extracted and converted into the standard rules of the intrusion detection system for subsequent defense. The experimental results show that the new method solves the stagnation problem of the classical ant optimal algorithm, extends the search space, extracts signatures more efficiently and precisely, and reduces the false positive rate and the false negative rate.

The deviation of the aero-engine on-board adaptive system model could not be completely eliminated, which may result in serious estimation deviation and filtering divergence. A new Kalman estimation algorithm with fading factor was proposed. Adjusting the weight of innovation covariance and increasing the effect on realistic measurement data in state estimation, the accuracy of aero-engine parameters estimation was ensured. Compared with the conventional Kalman filtering, the simulation results shows that the method proposed can restrain filtering divergence and obtain the high accuracy of estimation and the short convergence time. The derivation of the new method is simple, the computation amount is little, and the engineering application value is high.

Based on the principle and characteristics of the Differential Power Analysis (DPA) attack, the kernel function was used to estimate the probability distribution density of the leakage of power consumption in the password chip work process. By calculating the mutual information between the attack model and the power leakage, when the guessed key was correct, this paper quantified the risk value of the password chip in the face of DPA attacks. The experiments show that the risk quantification method can be a good estimate of the correlation degree between the attack model and power leakage when the guessed key is correct and then provides important indicators to complete password chip risk evaluation.

Outsourcing data owner’s data to Database Services Provider (DSP) securely provides XML database service for companies and organizations, which is an important data service form in cloud computing. This paper proposed an improved polyphonic splitting scheme for XML database service(IPSS-XML). IPSS-XML overcame the drawback of low verifying efficiency in other existing schemes by adding an Assistant Verifying Data (AVD) to each non-leaf node at low cost. The improvement enhances query executing efficiency without breaking the confidentiality constraints.